ImageNet: A large-scale hierarchical image database
ImageNet: A large-scale hierarchical image database is a dataset published in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009). On theSindex it has a DataRank of 30.6, placing it in the top 0.7% of the data-sharing corpus. It has been cited 61,487 times, with 193 citing works in its 1-hop citation network. Its calibrated FAIR score is 59/100.
Abstract
The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called "ImageNet", a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500–1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Indexed in repositories
- DataCite relations
- Linked datasets
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
1.6
From this paper's citation signal
Citation Network Contribution
28.9
From 193 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- ImageNet Large Scale Visual Recognition ChallengeInternational Journal of Computer Vision201540,012 citationsDataRank 1.6
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows2021 IEEE/CVF International Conference on Computer Vision (ICCV)202129,031 citationsDataRank 1.5
- YOLO9000: Better, Faster, Stronger2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)201718,798 citationsDataRank 1.5
- Generative adversarial networksCommunications of the ACM202013,214 citationsDataRank 1.4
- Momentum Contrast for Unsupervised Visual Representation Learning2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)202011,801 citationsDataRank 1.4
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 5% comes from its base citations and 95% from the citation network (193 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.